Expansion_Urbana_AMSS.ipynb

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  "metadata": {
    "colab": {
      "name": "Expansion Urbana AMSS.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "0dHRZFhfbrgg"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CesarRosales16/urban-sprawl-sv/blob/main/Expansion_Urbana_AMSS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análisis de la expansión urbana en el AMSS usando técnicas de Teledetección y Machine Learning\n",
        "\n",
        "El Notebook, el código fuente generado, las fuentes de datos utilizadas, así como los resultados obtenidos y recursos adicionales se encuentran en el repositorio de Github: https://github.com/CesarRosales16/urban-sprawl-sv"
      ],
      "metadata": {
        "id": "6eHyVF0UEUp6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparación del ambiente de desarrollo"
      ],
      "metadata": {
        "id": "33IP3QrLJ5e8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se instala GeoPandas ya que por el momento no forma parte de las dependencias incluidas por defecto en Google Colab."
      ],
      "metadata": {
        "id": "U1TAFPNGKaMt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install geopandas"
      ],
      "metadata": {
        "id": "jGUV9TiqKQSS",
        "outputId": "30363720-53f8-4787-c8d8-9c7fe906c3d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting geopandas\n",
            "  Downloading geopandas-0.10.2-py2.py3-none-any.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 5.1 MB/s \n",
            "\u001b[?25hCollecting fiona>=1.8\n",
            "  Downloading Fiona-1.8.21-cp37-cp37m-manylinux2014_x86_64.whl (16.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 16.7 MB 320 kB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.3.5)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.2)\n",
            "Collecting pyproj>=2.2.0\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 9.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.4.0)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2022.6.15)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2022.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se importan las librerias necesarias."
      ],
      "metadata": {
        "id": "Cd6MA0BWKnFE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import ee \n",
        "import folium \n",
        "import pandas as pd\n",
        "import geopandas as gpd \n",
        "import json\n",
        "from google.colab import drive\n",
        "import time \n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "M6iX7atVJGar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se inicializan y autentican las librerias que lo requieren."
      ],
      "metadata": {
        "id": "6F6tZn5mMq39"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trigger the EE authentication flow.\n",
        "ee.Authenticate()\n",
        "\n",
        "# Initialize the EE library.\n",
        "ee.Initialize()"
      ],
      "metadata": {
        "id": "xt9SWu0OMO_l",
        "outputId": "e316d001-85fb-420a-f824-9d9faa581b70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To authorize access needed by Earth Engine, open the following URL in a web browser and follow the instructions. If the web browser does not start automatically, please manually browse the URL below.\n",
            "\n",
            "    https://code.earthengine.google.com/client-auth?scopes=https%3A//www.googleapis.com/auth/earthengine%20https%3A//www.googleapis.com/auth/devstorage.full_control&request_id=raLd1hVkL1HAEVlxeVb8bHfOa2fWCuDOF_XuYe9NLbE&tc=GBwfXdUcHLxILoi1hMO7gsiTZ1dwWKj4fEJ1JkALtEw&cc=mVppaSJozDc-sf22D_b4Nh-yHD-ceKrpJd2__T6OPfI\n",
            "\n",
            "The authorization workflow will generate a code, which you should paste in the box below. \n",
            "Enter verification code: 4/1AX4XfWi2FP5MmiZYCgsMvkr7ulpmvWAslcQYlGIcpiMXG40EkxnV-PCihUY\n",
            "\n",
            "Successfully saved authorization token.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We mount drive to acces to our folders\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "ohhdyjJqMeVa",
        "outputId": "49734d3d-e422-442f-ee19-b30a814feacc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Delimitación espacial"
      ],
      "metadata": {
        "id": "VvnAkyGASa6s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a method for displaying Earth Engine image tiles to folium map.\n",
        "def add_ee_layer(self, ee_image_object, vis_params, name):\n",
        "  map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)\n",
        "  folium.raster_layers.TileLayer(\n",
        "    tiles = map_id_dict['tile_fetcher'].url_format,\n",
        "    attr = 'Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n",
        "    name = name,\n",
        "    overlay = True,\n",
        "    control = True\n",
        "  ).add_to(self)\n",
        "\n",
        "# Add EE drawing method to folium.\n",
        "folium.Map.add_ee_layer = add_ee_layer"
      ],
      "metadata": {
        "id": "0SVMnvxWEl_1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data from shapefile (.shp) located at GitHub\n",
        "# https://github.com/CesarRosales16/Urban-Sprawl-SV\n",
        "mun_datasource_url = 'https://raw.githubusercontent.com/CesarRosales16/Urban-Sprawl-SV/main/datasources/municipios-qgis/municipios_wgs84.shp'\n",
        "mun_shp_path = '/vsicurl/' + mun_datasource_url\n",
        "mun_data = gpd.read_file(mun_shp_path)\n",
        "mun_data.head()"
      ],
      "metadata": {
        "id": "_wBU1X-GFBD1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "6ea8cbd9-192a-4000-c1d8-add72ee655b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   fid      NOM_DPTO COD_DPTO           NOM_MUN COD_MUN COD_MUN4  TASA_POBR  \\\n",
              "0  1.0  CHALATENANGO       04  NUEVA CONCEPCION      16     0416       46.6   \n",
              "1  2.0  CHALATENANGO       04     AGUA CALIENTE      01     0401       57.3   \n",
              "2  3.0  CHALATENANGO       04          LA PALMA      12     0412       52.4   \n",
              "3  4.0  CHALATENANGO       04       SAN IGNACIO      25     0425       55.2   \n",
              "4  5.0  CHALATENANGO       04            CITALA      04     0404       55.7   \n",
              "\n",
              "         POBR_FISDL    PERIM_KM    AREA_KM2  POB_TOTAL  POB_HOMB  POB_MUJER  \\\n",
              "0  Pobreza Moderada  111.054433  343.809219      28625     13491      15134   \n",
              "1      Pobreza Alta   59.338583  128.497304       8261      3820       4441   \n",
              "2      Pobreza Alta   75.920713  150.504556      12235      5781       6454   \n",
              "3      Pobreza Alta   50.199616   57.706536       8611      4055       4556   \n",
              "4      Pobreza Alta   59.140700   73.623486       4164      1964       2200   \n",
              "\n",
              "   Shape_Leng  Shape_Area                                           geometry  \n",
              "0    1.015356    0.028779  POLYGON ((-89.28916 14.24249, -89.28889 14.241...  \n",
              "1    0.541842    0.010761  POLYGON ((-89.23636 14.27155, -89.23595 14.271...  \n",
              "2    0.696167    0.012609  POLYGON ((-89.10319 14.35704, -89.10298 14.356...  \n",
              "3    0.460160    0.004835  POLYGON ((-89.08244 14.35526, -89.08251 14.355...  \n",
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              "      <th>fid</th>\n",
              "      <th>NOM_DPTO</th>\n",
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              "      <th>NOM_MUN</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>CHALATENANGO</td>\n",
              "      <td>04</td>\n",
              "      <td>NUEVA CONCEPCION</td>\n",
              "      <td>16</td>\n",
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              "      <td>Pobreza Moderada</td>\n",
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              "      <td>POLYGON ((-89.28916 14.24249, -89.28889 14.241...</td>\n",
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              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>CHALATENANGO</td>\n",
              "      <td>04</td>\n",
              "      <td>AGUA CALIENTE</td>\n",
              "      <td>01</td>\n",
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              "      <td>57.3</td>\n",
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              "      <td>8261</td>\n",
              "      <td>3820</td>\n",
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              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>CHALATENANGO</td>\n",
              "      <td>04</td>\n",
              "      <td>LA PALMA</td>\n",
              "      <td>12</td>\n",
              "      <td>0412</td>\n",
              "      <td>52.4</td>\n",
              "      <td>Pobreza Alta</td>\n",
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              "      <td>150.504556</td>\n",
              "      <td>12235</td>\n",
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              "      <td>POLYGON ((-89.10319 14.35704, -89.10298 14.356...</td>\n",
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              "      <td>CHALATENANGO</td>\n",
              "      <td>04</td>\n",
              "      <td>SAN IGNACIO</td>\n",
              "      <td>25</td>\n",
              "      <td>0425</td>\n",
              "      <td>55.2</td>\n",
              "      <td>Pobreza Alta</td>\n",
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              "      <td>CHALATENANGO</td>\n",
              "      <td>04</td>\n",
              "      <td>CITALA</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set coordinate reference system to dataset (WGS84 = epsg:4326)\n",
        "mun_data.crs = 'epsg:4326'\n",
        "print(mun_data.crs)\n",
        "mun_data.plot()"
      ],
      "metadata": {
        "id": "yGGt0mJqFFWd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "9eafe183-664d-417f-b64d-4edefc05a248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epsg:4326\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa638448410>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new dataset based on AMSS municipalities\n",
        "mun_amss = ['NEJAPA', 'TONACATEPEQUE', 'CUSCATANCINGO', 'APOPA', 'DELGADO', 'AYUTUXTEPEQUE', 'MEJICANOS', 'SAN SALVADOR',\n",
        "'SAN MARTIN', 'ILOPANGO', 'SOYAPANGO', 'SAN MARCOS', 'ANTIGUO CUSCATLAN', 'SANTA TECLA']\n",
        "amss_data = mun_data.loc[mun_data.NOM_MUN.isin(mun_amss)]"
      ],
      "metadata": {
        "id": "u7jco1S8FHyT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new dataset based on aiditional municipalities, for our case Colon\n",
        "mun_aditional = ['COLON']\n",
        "mun_aditional_data = mun_data.loc[mun_data.NOM_MUN.isin(mun_aditional)]"
      ],
      "metadata": {
        "id": "NptAejDXFKXM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot amss and colon in a single map to check geometries\n",
        "ax = amss_data.plot(figsize=(5,5), color='none', edgecolor='gray')\n",
        "mun_aditional_data.plot(ax=ax, color='none', edgecolor='red')"
      ],
      "metadata": {
        "id": "gm3_ddaWFNFj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "b2122e9c-52cb-4cdd-a1ec-2558713b1d6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa637bf3110>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge AMSS and aditional municipalities in a single dataframe for our ROI (Region Of Interest)\n",
        "mun_roi = mun_amss + mun_aditional\n",
        "roi_data = mun_data.loc[mun_data.NOM_MUN.isin(mun_roi)]"
      ],
      "metadata": {
        "id": "EwaQoVBMFOEt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the CRS for the final df\n",
        "print(roi_data.crs)\n",
        "# Plot our ROI\n",
        "roi_data.plot(figsize=(5,5), color='none', edgecolor='gray')"
      ],
      "metadata": {
        "id": "eMVochzzFPab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "4588a2c4-6c42-4063-f672-5f2d439b7375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epsg:4326\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa637c10c50>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Geographic data for SV\n",
        "sv_lon = -88.89653\n",
        "sv_lat = 13.794185\n",
        "sv_poi = ee.Geometry.Point(sv_lon, sv_lat)"
      ],
      "metadata": {
        "id": "ncPGEAqAG25M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Geographic data for AMSS\n",
        "amss_lon = -89.214763\n",
        "amss_lat = 13.723560\n",
        "amss_poi = ee.Geometry.Point(amss_lon, amss_lat)"
      ],
      "metadata": {
        "id": "mRhyuQRmnMpB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create json from our ROI geodataframe\n",
        "roi_json = json.loads(roi_data.to_json())\n",
        "\n",
        "# Craete Google Earth Engine FeatureCollection from geojson\n",
        "roi_fc = ee.FeatureCollection(roi_json)\n",
        "\n",
        "# Create GEE Geometry from FeatureCollection\n",
        "roi_geom = ee.Geometry(roi_fc.geometry())"
      ],
      "metadata": {
        "id": "0EoewHFbH82s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recortando imágenes Landsat"
      ],
      "metadata": {
        "id": "NbVpPLSPR1p7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that retrieves Landsat Collection ID for a given year\n",
        "def getLandsatCollectionId(year):\n",
        "  if year >= 1985 and year <= 1999:\n",
        "    return 'LT05'\n",
        "  elif year >= 2000 and year <= 2013:\n",
        "    return 'LE07'\n",
        "  elif year >= 2014 and year <= 2021:\n",
        "    return 'LC08'"
      ],
      "metadata": {
        "id": "-TnXdDICJiBz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define cloud mask for Landsat 4/5/7 images\n",
        "def cloudMaskL457(img):\n",
        "  '''  \n",
        "  Function to mask clouds based on the pixel_qa band of Landsat 4/5/7 SR data. See:\n",
        "  https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_SR\n",
        "  \n",
        "  Params:\n",
        "  -------\n",
        "  - img: image input Landsat 4/5/7 SR image\n",
        "  \n",
        "  Return:\n",
        "  -------\n",
        "  cloudmasked Landsat 4/5/7 image\n",
        "  '''\n",
        "  # Get the pixel QA band.\n",
        "  qa = img.select('pixel_qa')\n",
        "  # If the cloud bit (5) is set and the cloud confidence (7) is high or the cloud shadow bit is set (3), then it's a bad pixel.\n",
        "  cloud = qa.bitwiseAnd(1 << 5)\\\n",
        "                  .And(qa.bitwiseAnd(1 << 7))\\\n",
        "                  .Or(qa.bitwiseAnd(1 << 3))\n",
        "  # Remove edge pixels that don't occur in all bands\n",
        "  mask = img.mask().reduce(ee.Reducer.min())\n",
        "  return img.updateMask(cloud.Not()).updateMask(mask)  "
      ],
      "metadata": {
        "id": "lOpKuwZ1G_5E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define cloud mask for Landsat 8 images\n",
        "def cloudMaskL8(img):\n",
        "  '''  \n",
        "  Function to mask clouds based on the pixel_qa band of Landsat 8 SR data. See:\n",
        "  https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_SR\n",
        "  \n",
        "  Params:\n",
        "  -------\n",
        "  - img: image input Landsat 8 SR image\n",
        "  \n",
        "  Return:\n",
        "  -------\n",
        "  cloudmasked Landsat 8 image\n",
        "  '''\n",
        "  cloudShadowBitMask = (1 << 3)\n",
        "  cloudsBitMask = (1 << 5)\n",
        "  # Get the pixel QA band.\n",
        "  qa = img.select('pixel_qa')\n",
        "  # Both flags should be set to zero, indicating clear conditions.\n",
        "  mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)\\\n",
        "           .And(qa.bitwiseAnd(cloudsBitMask).eq(0))\n",
        "  return img.updateMask(mask)    "
      ],
      "metadata": {
        "id": "04whIroOlOqZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that clips a single Landsat Image given landsat collection ID, a year, a roi and a porcentage of cloud contamination\n",
        "def clipLandsatImage(year, landsat_id, cloud_contamination, roi):\n",
        "\n",
        "  landsat_path = 'LANDSAT/' + landsat_id + '/C01/T1_SR'\n",
        "  initial_date = str(year) + '-01-01'\n",
        "  end_date = str(year) + '-12-31'\n",
        "\n",
        "  landsat_collection = ee.ImageCollection(landsat_path)\n",
        "\n",
        "  clipped_image = landsat_collection\\\n",
        "    .filterBounds(roi)\\\n",
        "    .filterDate(initial_date, end_date)\\\n",
        "    .filter(ee.Filter.lt('CLOUD_COVER', cloud_contamination))\\\n",
        "    .map(cloudMaskL8 if landsat_id == 'LC08' else cloudMaskL457)\\\n",
        "    .median()\\\n",
        "    .clip(roi)\n",
        "\n",
        "  return clipped_image"
      ],
      "metadata": {
        "id": "tvVJqza8J2G1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that clips n Landsat Images in a range of years, given a cloud contamination percentage and a roi\n",
        "def clipLandsatImagesInRange(initial_year, end_year, cloud_contamination, roi):\n",
        "  clipped_images = []\n",
        "  for year in range(initial_year, end_year + 1):\n",
        "    landsat_id = getLandsatCollectionId(year)\n",
        "    clipped_images.append(clipLandsatImage(year, landsat_id, cloud_contamination, roi_geom))\n",
        "  return clipped_images"
      ],
      "metadata": {
        "id": "zRnceTQVKzbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that clips n Landsat Images in a range of years, given a cloud contamination percentage and a roi\n",
        "def clipLandsatImagesInRangeList(range_years, cloud_contamination, roi):\n",
        "  clipped_images = []\n",
        "  for year in range_years:\n",
        "    landsat_id = getLandsatCollectionId(year)\n",
        "    clipped_images.append(clipLandsatImage(year, landsat_id, cloud_contamination, roi_geom))\n",
        "  return clipped_images"
      ],
      "metadata": {
        "id": "TyyGhbMayRag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#clipped_images = clipLandsatImagesInRange(initial_year=1992, end_year=2021, cloud_contamination=30, roi=roi_geom)"
      ],
      "metadata": {
        "id": "5BCff-X20Azn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We define our years of interest for Landsat Imagery\n",
        "landsat5_years = [1991, 1993, 1994, 1995, 1996, 1997, 1998, 1999]\n",
        "landsat7_years = [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013]\n",
        "landsat8_years = [2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021]\n",
        "\n",
        "# Clip landsat images for given year, cloud contamination and ROI geometry\n",
        "clipped_images_L5 = clipLandsatImagesInRangeList(landsat5_years, 20, roi_geom)\n",
        "clipped_images_L7 = clipLandsatImagesInRangeList(landsat7_years, 20, roi_geom)\n",
        "clipped_images_L8 = clipLandsatImagesInRangeList(landsat8_years, 20, roi_geom)\n",
        "\n",
        "# Concatenate results in a global list \n",
        "years_of_interes = landsat5_years + landsat7_years + landsat8_years\n",
        "clipped_images = clipped_images_L5 + clipped_images_L7 + clipped_images_L8\n",
        "\n",
        "print('Number of clipped images:', len(clipped_images))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaQYAZdsNRUc",
        "outputId": "af0002b4-4a72-4997-da00-e06b487c5805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clipped images: 30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clasificando las imágenes Landsat"
      ],
      "metadata": {
        "id": "1-E9iTYxSk1O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preparación previa a la clasificación"
      ],
      "metadata": {
        "id": "04moz16U-aon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieving training points from GitHub\n",
        "train_datasource_url = 'https://raw.githubusercontent.com/CesarRosales16/Urban-Sprawl-SV/main/datasources/train-data-1991/TrainCoverAMSSC.shp'\n",
        "train_shp_path = '/vsicurl/' + train_datasource_url\n",
        "train_data = gpd.read_file(train_shp_path)\n",
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "J4RiFooI-eeE",
        "outputId": "8238ccaa-35f9-485b-ce05-7dd02e7920e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Class                    geometry\n",
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    {
      "cell_type": "code",
      "source": [
        "# Create json from geodataframe\n",
        "train_json = json.loads(train_data.to_json())"
      ],
      "metadata": {
        "id": "SrEh5cxc-zsz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing training data\n",
        "training = ee.FeatureCollection(train_json)\n",
        "\n",
        "label = 'Class'\n",
        "bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']"
      ],
      "metadata": {
        "id": "s5ECGPtR-3gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classifyGivenImage(classifier, image, train_pct, training, label, bands):\n",
        "  input = image.select(bands)\n",
        "\n",
        "  train_image = input.sampleRegions(**{\n",
        "      'collection': training,\n",
        "      'properties': [label],\n",
        "      'scale': 30\n",
        "  })\n",
        "\n",
        "  training_data = train_image.randomColumn()\n",
        "\n",
        "  train_set = training_data.filter(ee.Filter.lessThan('random', train_pct))\n",
        "  test_set = training_data.filter(ee.Filter.greaterThanOrEquals('random', train_pct))\n",
        "\n",
        "  # Train our model with the given classifier for the train set\n",
        "  model = classifier.train(train_set, label, bands)\n",
        "\n",
        "  # Classify entire image\n",
        "  classified_image = input.classify(model)\n",
        "\n",
        "  #Validation:\n",
        "  validated = test_set.classify(model)\n",
        "\n",
        "  error_matrix = validated.errorMatrix(label, 'classification')\n",
        "  overall_accuracy = error_matrix.accuracy().getInfo()\n",
        "  kappa_index = error_matrix.kappa().getInfo()\n",
        "\n",
        "  return classified_image, error_matrix.getInfo(), overall_accuracy, kappa_index"
      ],
      "metadata": {
        "id": "RrU-FcLgDSlH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classifyMultipleImages(classifier, images, train_pct, training, label, bands):\n",
        "  classified_images = []\n",
        "  error_matrices = []\n",
        "  overall_accuracies = []\n",
        "  kappa_indexes = []\n",
        "  for image in images:\n",
        "    print('Classifying image {} ...'.format(len(classified_images)+1))\n",
        "    classified, error_matrix, overall_accuracy, kappa_index = classifyGivenImage(classifier, image, train_pct, training, label, bands)\n",
        "    classified_images.append(classified)\n",
        "    error_matrices.append(error_matrix)\n",
        "    overall_accuracies.append(overall_accuracy)\n",
        "    kappa_indexes.append(kappa_index)\n",
        "  return classified_images, error_matrices, overall_accuracies, kappa_indexes"
      ],
      "metadata": {
        "id": "90QU2zT7VQpQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CART"
      ],
      "metadata": {
        "id": "fkr14Qt6CsTk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cart_classifier = ee.Classifier.smileCart()\n",
        "classified_images_cart, error_matrices_cart, overall_accuracies_cart, kappa_indexes_cart = classifyMultipleImages(cart_classifier, clipped_images, 0.80, training, label, bands)"
      ],
      "metadata": {
        "id": "2geEnx0PC2sF",
        "outputId": "5466001c-c743-43ec-dc5c-71e36e6ed6b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifying image 1 ...\n",
            "Classifying image 2 ...\n",
            "Classifying image 3 ...\n",
            "Classifying image 4 ...\n",
            "Classifying image 5 ...\n",
            "Classifying image 6 ...\n",
            "Classifying image 7 ...\n",
            "Classifying image 8 ...\n",
            "Classifying image 9 ...\n",
            "Classifying image 10 ...\n",
            "Classifying image 11 ...\n",
            "Classifying image 12 ...\n",
            "Classifying image 13 ...\n",
            "Classifying image 14 ...\n",
            "Classifying image 15 ...\n",
            "Classifying image 16 ...\n",
            "Classifying image 17 ...\n",
            "Classifying image 18 ...\n",
            "Classifying image 19 ...\n",
            "Classifying image 20 ...\n",
            "Classifying image 21 ...\n",
            "Classifying image 22 ...\n",
            "Classifying image 23 ...\n",
            "Classifying image 24 ...\n",
            "Classifying image 25 ...\n",
            "Classifying image 26 ...\n",
            "Classifying image 27 ...\n",
            "Classifying image 28 ...\n",
            "Classifying image 29 ...\n",
            "Classifying image 30 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_dict = {\n",
        "    'year': years_of_interes,\n",
        "    'overall_accuracy': overall_accuracies_cart,\n",
        "    'kappa_index': kappa_indexes_cart,\n",
        "    'error_matrices': error_matrices_cart\n",
        "}\n",
        "\n",
        "result_cart_df = pd.DataFrame(result_dict)\n",
        "result_cart_df.head()"
      ],
      "metadata": {
        "id": "mh_B2a77DC6t",
        "outputId": "a5c90644-d657-4a88-a906-7a4ae8cd356b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  overall_accuracy  kappa_index                        error_matrices\n",
              "0  1991          0.946809     0.902287   [[29, 3, 0], [2, 52, 0], [0, 0, 8]]\n",
              "1  1993          0.938272     0.891942   [[25, 1, 0], [4, 42, 0], [0, 0, 9]]\n",
              "2  1994          0.927083     0.853595  [[19, 6, 0], [1, 60, 0], [0, 0, 10]]\n",
              "3  1995          0.935065     0.855805   [[13, 2, 0], [3, 52, 0], [0, 0, 7]]\n",
              "4  1996          0.946809     0.889697   [[19, 3, 0], [2, 61, 0], [0, 0, 9]]"
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1991</td>\n",
              "      <td>0.946809</td>\n",
              "      <td>0.902287</td>\n",
              "      <td>[[29, 3, 0], [2, 52, 0], [0, 0, 8]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1993</td>\n",
              "      <td>0.938272</td>\n",
              "      <td>0.891942</td>\n",
              "      <td>[[25, 1, 0], [4, 42, 0], [0, 0, 9]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1994</td>\n",
              "      <td>0.927083</td>\n",
              "      <td>0.853595</td>\n",
              "      <td>[[19, 6, 0], [1, 60, 0], [0, 0, 10]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995</td>\n",
              "      <td>0.935065</td>\n",
              "      <td>0.855805</td>\n",
              "      <td>[[13, 2, 0], [3, 52, 0], [0, 0, 7]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1996</td>\n",
              "      <td>0.946809</td>\n",
              "      <td>0.889697</td>\n",
              "      <td>[[19, 3, 0], [2, 61, 0], [0, 0, 9]]</td>\n",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              year  overall_accuracy  kappa_index\n",
              "count    30.000000         30.000000    30.000000\n",
              "mean   2006.466667          0.935364     0.875422\n",
              "std       8.861903          0.026126     0.051949\n",
              "min    1991.000000          0.886598     0.764535\n",
              "25%    1999.250000          0.915337     0.839710\n",
              "50%    2006.500000          0.939374     0.883432\n",
              "75%    2013.750000          0.951397     0.910854\n",
              "max    2021.000000          0.989583     0.981363"
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              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>overall_accuracy</th>\n",
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              "      <th>count</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2006.466667</td>\n",
              "      <td>0.935364</td>\n",
              "      <td>0.875422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.861903</td>\n",
              "      <td>0.026126</td>\n",
              "      <td>0.051949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1991.000000</td>\n",
              "      <td>0.886598</td>\n",
              "      <td>0.764535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1999.250000</td>\n",
              "      <td>0.915337</td>\n",
              "      <td>0.839710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2006.500000</td>\n",
              "      <td>0.939374</td>\n",
              "      <td>0.883432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2013.750000</td>\n",
              "      <td>0.951397</td>\n",
              "      <td>0.910854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2021.000000</td>\n",
              "      <td>0.989583</td>\n",
              "      <td>0.981363</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
          "execution_count": 31
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    },
    {
      "cell_type": "code",
      "source": [
        "print(result_cart_df)"
      ],
      "metadata": {
        "id": "IXKumOoNDNe0",
        "outputId": "1764aa0a-1871-4aef-8582-7fce7e28b6c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    year  overall_accuracy  kappa_index                        error_matrices\n",
            "0   1991          0.946809     0.902287   [[29, 3, 0], [2, 52, 0], [0, 0, 8]]\n",
            "1   1993          0.938272     0.891942   [[25, 1, 0], [4, 42, 0], [0, 0, 9]]\n",
            "2   1994          0.927083     0.853595  [[19, 6, 0], [1, 60, 0], [0, 0, 10]]\n",
            "3   1995          0.935065     0.855805   [[13, 2, 0], [3, 52, 0], [0, 0, 7]]\n",
            "4   1996          0.946809     0.889697   [[19, 3, 0], [2, 61, 0], [0, 0, 9]]\n",
            "5   1997          0.943925     0.898063  [[32, 6, 0], [0, 59, 0], [0, 0, 10]]\n",
            "6   1998          0.907407     0.830082  [[22, 6, 0], [4, 61, 0], [0, 0, 15]]\n",
            "7   1999          0.935484     0.884280  [[23, 0, 0], [6, 52, 0], [0, 0, 12]]\n",
            "8   2000          0.989583     0.981363   [[33, 0, 0], [1, 53, 0], [0, 0, 9]]\n",
            "9   2001          0.942529     0.895983   [[25, 3, 0], [2, 48, 0], [0, 0, 9]]\n",
            "10  2002          0.987952     0.975603   [[24, 0, 0], [1, 53, 0], [0, 0, 5]]\n",
            "11  2003          0.968085     0.935247   [[24, 1, 0], [2, 60, 0], [0, 0, 7]]\n",
            "12  2004          0.952941     0.910808   [[26, 4, 0], [0, 49, 0], [0, 0, 6]]\n",
            "13  2005          0.951456     0.913197  [[29, 3, 0], [2, 57, 0], [0, 0, 12]]\n",
            "14  2006          0.914894     0.847465  [[20, 5, 0], [3, 52, 0], [0, 0, 14]]\n",
            "15  2007          0.921569     0.837126   [[23, 6, 0], [2, 64, 0], [0, 0, 7]]\n",
            "16  2008          0.940476     0.882583   [[24, 1, 0], [4, 50, 0], [0, 0, 5]]\n",
            "17  2009          0.963415     0.930860   [[22, 2, 0], [1, 49, 0], [0, 0, 8]]\n",
            "18  2010          0.916667     0.822731   [[22, 3, 0], [4, 52, 0], [0, 0, 3]]\n",
            "19  2011          0.888889     0.764535   [[22, 5, 0], [7, 67, 0], [0, 0, 7]]\n",
            "20  2012          0.911765     0.830659  [[22, 5, 0], [4, 60, 0], [0, 0, 11]]\n",
            "21  2013          0.886598     0.783789  [[18, 4, 0], [7, 56, 0], [0, 0, 12]]\n",
            "22  2014          0.955056     0.913086   [[23, 2, 0], [2, 54, 0], [0, 0, 8]]\n",
            "23  2015          0.914894     0.854207  [[33, 4, 0], [4, 43, 0], [0, 0, 10]]\n",
            "24  2016          0.890110     0.797957  [[18, 9, 0], [1, 51, 0], [0, 0, 12]]\n",
            "25  2017          0.928571     0.864284  [[26, 5, 0], [3, 66, 0], [0, 0, 12]]\n",
            "26  2018          0.954023     0.911585   [[21, 3, 0], [1, 53, 0], [0, 0, 9]]\n",
            "27  2019          0.907216     0.810875   [[19, 5, 0], [4, 60, 0], [0, 0, 9]]\n",
            "28  2020          0.951220     0.910870   [[24, 2, 0], [2, 46, 0], [0, 0, 8]]\n",
            "29  2021          0.942149     0.882099   [[29, 3, 0], [4, 76, 0], [0, 0, 9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_cart_df.to_csv('class_cart_results.csv')"
      ],
      "metadata": {
        "id": "NEG5UZt-DOX0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest"
      ],
      "metadata": {
        "id": "rQVHqTUxQxyn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_classifier = ee.Classifier.smileRandomForest(10)\n",
        "classified_images_rf, error_matrices_rf, overall_accuracies_rf, kappa_indexes_rf = classifyMultipleImages(rf_classifier, clipped_images, 0.80, training, label, bands)"
      ],
      "metadata": {
        "id": "CR1KGFBBW1hm",
        "outputId": "f5ea3437-1d5d-44f6-95ab-b5c61ae8fe0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifying image 1 ...\n",
            "Classifying image 2 ...\n",
            "Classifying image 3 ...\n",
            "Classifying image 4 ...\n",
            "Classifying image 5 ...\n",
            "Classifying image 6 ...\n",
            "Classifying image 7 ...\n",
            "Classifying image 8 ...\n",
            "Classifying image 9 ...\n",
            "Classifying image 10 ...\n",
            "Classifying image 11 ...\n",
            "Classifying image 12 ...\n",
            "Classifying image 13 ...\n",
            "Classifying image 14 ...\n",
            "Classifying image 15 ...\n",
            "Classifying image 16 ...\n",
            "Classifying image 17 ...\n",
            "Classifying image 18 ...\n",
            "Classifying image 19 ...\n",
            "Classifying image 20 ...\n",
            "Classifying image 21 ...\n",
            "Classifying image 22 ...\n",
            "Classifying image 23 ...\n",
            "Classifying image 24 ...\n",
            "Classifying image 25 ...\n",
            "Classifying image 26 ...\n",
            "Classifying image 27 ...\n",
            "Classifying image 28 ...\n",
            "Classifying image 29 ...\n",
            "Classifying image 30 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_dict = {\n",
        "    'year': years_of_interes,\n",
        "    'overall_accuracy': overall_accuracies_rf,\n",
        "    'kappa_index': kappa_indexes_rf,\n",
        "    'error_matrices': error_matrices_rf\n",
        "}\n",
        "\n",
        "result_rf_df = pd.DataFrame(result_dict)\n",
        "result_rf_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JdajBP2raarP",
        "outputId": "6e645014-35cf-4093-905a-9ed5745510e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  overall_accuracy  kappa_index                        error_matrices\n",
              "0  1991          0.936170     0.883278   [[29, 3, 0], [3, 51, 0], [0, 0, 8]]\n",
              "1  1993          0.975309     0.956545   [[26, 0, 0], [2, 44, 0], [0, 0, 9]]\n",
              "2  1994          0.947917     0.895425  [[20, 5, 0], [0, 61, 0], [0, 0, 10]]\n",
              "3  1995          0.961039     0.910811   [[13, 2, 0], [1, 54, 0], [0, 0, 7]]\n",
              "4  1996          0.968085     0.933818   [[20, 2, 0], [1, 62, 0], [0, 0, 9]]"
            ],
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              "\n",
              "  <div id=\"df-d46b6407-c86b-49b4-bb9d-d8b782ba0b83\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>overall_accuracy</th>\n",
              "      <th>kappa_index</th>\n",
              "      <th>error_matrices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1991</td>\n",
              "      <td>0.936170</td>\n",
              "      <td>0.883278</td>\n",
              "      <td>[[29, 3, 0], [3, 51, 0], [0, 0, 8]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1993</td>\n",
              "      <td>0.975309</td>\n",
              "      <td>0.956545</td>\n",
              "      <td>[[26, 0, 0], [2, 44, 0], [0, 0, 9]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1994</td>\n",
              "      <td>0.947917</td>\n",
              "      <td>0.895425</td>\n",
              "      <td>[[20, 5, 0], [0, 61, 0], [0, 0, 10]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995</td>\n",
              "      <td>0.961039</td>\n",
              "      <td>0.910811</td>\n",
              "      <td>[[13, 2, 0], [1, 54, 0], [0, 0, 7]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1996</td>\n",
              "      <td>0.968085</td>\n",
              "      <td>0.933818</td>\n",
              "      <td>[[20, 2, 0], [1, 62, 0], [0, 0, 9]]</td>\n",
              "    </tr>\n",
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              "                                                     [key], {});\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_rf_df.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "tM5fq1a1bVdQ",
        "outputId": "bad20af7-77c1-4f59-fecc-b5d56164a35f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              year  overall_accuracy  kappa_index\n",
              "count    30.000000         30.000000    30.000000\n",
              "mean   2006.466667          0.952429     0.908389\n",
              "std       8.861903          0.021160     0.040113\n",
              "min    1991.000000          0.901099     0.821140\n",
              "25%    1999.250000          0.943314     0.886315\n",
              "50%    2006.500000          0.953322     0.910809\n",
              "75%    2013.750000          0.966098     0.934933\n",
              "max    2021.000000          0.989583     0.981363"
            ],
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              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>overall_accuracy</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2006.466667</td>\n",
              "      <td>0.952429</td>\n",
              "      <td>0.908389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.861903</td>\n",
              "      <td>0.021160</td>\n",
              "      <td>0.040113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1991.000000</td>\n",
              "      <td>0.901099</td>\n",
              "      <td>0.821140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1999.250000</td>\n",
              "      <td>0.943314</td>\n",
              "      <td>0.886315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2006.500000</td>\n",
              "      <td>0.953322</td>\n",
              "      <td>0.910809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2013.750000</td>\n",
              "      <td>0.966098</td>\n",
              "      <td>0.934933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2021.000000</td>\n",
              "      <td>0.989583</td>\n",
              "      <td>0.981363</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b587623c-bcd8-4d5d-945b-fa844d4c6e59');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result_rf_df)"
      ],
      "metadata": {
        "id": "L9-ooxv1Q_5u",
        "outputId": "4a426b80-6f2c-4c8a-cec2-d206d6dddc73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    year  overall_accuracy  kappa_index                        error_matrices\n",
            "0   1991          0.936170     0.883278   [[29, 3, 0], [3, 51, 0], [0, 0, 8]]\n",
            "1   1993          0.975309     0.956545   [[26, 0, 0], [2, 44, 0], [0, 0, 9]]\n",
            "2   1994          0.947917     0.895425  [[20, 5, 0], [0, 61, 0], [0, 0, 10]]\n",
            "3   1995          0.961039     0.910811   [[13, 2, 0], [1, 54, 0], [0, 0, 7]]\n",
            "4   1996          0.968085     0.933818   [[20, 2, 0], [1, 62, 0], [0, 0, 9]]\n",
            "5   1997          0.915888     0.848609  [[32, 6, 0], [3, 56, 0], [0, 0, 10]]\n",
            "6   1998          0.962963     0.932815  [[26, 2, 0], [2, 63, 0], [0, 0, 15]]\n",
            "7   1999          0.978495     0.959670  [[22, 1, 0], [1, 57, 0], [0, 0, 12]]\n",
            "8   2000          0.989583     0.981363   [[33, 0, 0], [1, 53, 0], [0, 0, 9]]\n",
            "9   2001          0.965517     0.936926   [[25, 3, 0], [0, 50, 0], [0, 0, 9]]\n",
            "10  2002          0.975904     0.951632   [[24, 0, 0], [2, 52, 0], [0, 0, 5]]\n",
            "11  2003          0.957447     0.912923   [[23, 2, 0], [2, 60, 0], [0, 0, 7]]\n",
            "12  2004          0.952941     0.910808   [[26, 4, 0], [0, 49, 0], [0, 0, 6]]\n",
            "13  2005          0.951456     0.913197  [[29, 3, 0], [2, 57, 0], [0, 0, 12]]\n",
            "14  2006          0.925532     0.868924  [[22, 3, 0], [4, 51, 0], [0, 0, 14]]\n",
            "15  2007          0.941176     0.881349   [[26, 3, 0], [3, 63, 0], [0, 0, 7]]\n",
            "16  2008          0.916667     0.832907   [[22, 3, 0], [4, 50, 0], [0, 0, 5]]\n",
            "17  2009          0.963415     0.930860   [[22, 2, 0], [1, 49, 0], [0, 0, 8]]\n",
            "18  2010          0.952381     0.897748   [[23, 2, 0], [2, 54, 0], [0, 0, 3]]\n",
            "19  2011          0.953704     0.899310   [[24, 3, 0], [2, 72, 0], [0, 0, 7]]\n",
            "20  2012          0.950980     0.905921  [[24, 3, 0], [2, 62, 0], [0, 0, 11]]\n",
            "21  2013          0.948454     0.898344  [[19, 3, 0], [2, 61, 0], [0, 0, 12]]\n",
            "22  2014          0.966292     0.935304   [[24, 1, 0], [2, 54, 0], [0, 0, 8]]\n",
            "23  2015          0.946809     0.909056  [[35, 2, 0], [3, 44, 0], [0, 0, 10]]\n",
            "24  2016          0.901099     0.821140  [[20, 7, 0], [2, 50, 0], [0, 0, 12]]\n",
            "25  2017          0.955357     0.915663  [[28, 3, 0], [2, 67, 0], [0, 0, 12]]\n",
            "26  2018          0.977011     0.956456   [[23, 1, 0], [1, 53, 0], [0, 0, 9]]\n",
            "27  2019          0.917526     0.833333   [[20, 4, 0], [4, 60, 0], [0, 0, 9]]\n",
            "28  2020          0.975610     0.955435   [[25, 1, 0], [1, 47, 0], [0, 0, 8]]\n",
            "29  2021          0.942149     0.882099   [[29, 3, 0], [4, 76, 0], [0, 0, 9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_rf_df.to_csv('class_rf_results.csv')"
      ],
      "metadata": {
        "id": "C62eZGovbfk2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SVM con kernel lineal"
      ],
      "metadata": {
        "id": "opvxF5tyCynC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm_classifier = ee.Classifier.libsvm()\n",
        "classified_images_svm, error_matrices_svm, overall_accuracies_svm, kappa_indexes_svm = classifyMultipleImages(svm_classifier, clipped_images, 0.80, training, label, bands)"
      ],
      "metadata": {
        "id": "kOrWhV7cC3L0",
        "outputId": "6d3dfaf9-6563-441c-c496-d66aaca97a7b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifying image 1 ...\n",
            "Classifying image 2 ...\n",
            "Classifying image 3 ...\n",
            "Classifying image 4 ...\n",
            "Classifying image 5 ...\n",
            "Classifying image 6 ...\n",
            "Classifying image 7 ...\n",
            "Classifying image 8 ...\n",
            "Classifying image 9 ...\n",
            "Classifying image 10 ...\n",
            "Classifying image 11 ...\n",
            "Classifying image 12 ...\n",
            "Classifying image 13 ...\n",
            "Classifying image 14 ...\n",
            "Classifying image 15 ...\n",
            "Classifying image 16 ...\n",
            "Classifying image 17 ...\n",
            "Classifying image 18 ...\n",
            "Classifying image 19 ...\n",
            "Classifying image 20 ...\n",
            "Classifying image 21 ...\n",
            "Classifying image 22 ...\n",
            "Classifying image 23 ...\n",
            "Classifying image 24 ...\n",
            "Classifying image 25 ...\n",
            "Classifying image 26 ...\n",
            "Classifying image 27 ...\n",
            "Classifying image 28 ...\n",
            "Classifying image 29 ...\n",
            "Classifying image 30 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_dict = {\n",
        "    'year': years_of_interes,\n",
        "    'overall_accuracy': overall_accuracies_svm,\n",
        "    'kappa_index': kappa_indexes_svm,\n",
        "    'error_matrices': error_matrices_svm\n",
        "}\n",
        "\n",
        "result_svm_df = pd.DataFrame(result_dict)\n",
        "result_svm_df.head()"
      ],
      "metadata": {
        "id": "UDKBe5B_DD-6",
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          "data": {
            "text/plain": [
              "   year  overall_accuracy  kappa_index                        error_matrices\n",
              "0  1991          0.627660     0.398537  [[29, 3, 0], [32, 22, 0], [0, 0, 8]]\n",
              "1  1993          0.444444     0.006270  [[3, 23, 0], [22, 24, 0], [0, 0, 9]]\n",
              "2  1994          0.927083     0.855856  [[20, 5, 0], [2, 59, 0], [0, 0, 10]]\n",
              "3  1995          0.961039     0.916000   [[15, 0, 0], [3, 52, 0], [0, 0, 7]]\n",
              "4  1996          0.957447     0.910900   [[19, 3, 0], [1, 62, 0], [0, 0, 9]]"
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              "      <td>1991</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1993</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1994</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995</td>\n",
              "      <td>0.961039</td>\n",
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              "      <th>4</th>\n",
              "      <td>1996</td>\n",
              "      <td>0.957447</td>\n",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              year  overall_accuracy  kappa_index\n",
              "count    30.000000         30.000000    30.000000\n",
              "mean   2006.466667          0.909601     0.833238\n",
              "std       8.861903          0.108956     0.188638\n",
              "min    1991.000000          0.444444     0.006270\n",
              "25%    1999.250000          0.909471     0.832011\n",
              "50%    2006.500000          0.934589     0.870135\n",
              "75%    2013.750000          0.960141     0.915973\n",
              "max    2021.000000          0.989583     0.981210"
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              "      <th>year</th>\n",
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              "      <th>mean</th>\n",
              "      <td>2006.466667</td>\n",
              "      <td>0.909601</td>\n",
              "      <td>0.833238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.861903</td>\n",
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              "      <td>0.188638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1991.000000</td>\n",
              "      <td>0.444444</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>0.832011</td>\n",
              "    </tr>\n",
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              "      <th>50%</th>\n",
              "      <td>2006.500000</td>\n",
              "      <td>0.934589</td>\n",
              "      <td>0.870135</td>\n",
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              "      <th>75%</th>\n",
              "      <td>2013.750000</td>\n",
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              "      <td>0.915973</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
              "      <td>2021.000000</td>\n",
              "      <td>0.989583</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-a05ff779-2488-437d-9448-bd5e888347ea');\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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    },
    {
      "cell_type": "code",
      "source": [
        "print(result_svm_df)"
      ],
      "metadata": {
        "id": "8nmJjZ5jDVa_",
        "outputId": "f37dcbed-1bf5-42ca-aae1-07ee91844767",
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          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    year  overall_accuracy  kappa_index                        error_matrices\n",
            "0   1991          0.627660     0.398537  [[29, 3, 0], [32, 22, 0], [0, 0, 8]]\n",
            "1   1993          0.444444     0.006270  [[3, 23, 0], [22, 24, 0], [0, 0, 9]]\n",
            "2   1994          0.927083     0.855856  [[20, 5, 0], [2, 59, 0], [0, 0, 10]]\n",
            "3   1995          0.961039     0.916000   [[15, 0, 0], [3, 52, 0], [0, 0, 7]]\n",
            "4   1996          0.957447     0.910900   [[19, 3, 0], [1, 62, 0], [0, 0, 9]]\n",
            "5   1997          0.953271     0.915894  [[34, 4, 0], [1, 58, 0], [0, 0, 10]]\n",
            "6   1998          0.907407     0.830082  [[22, 6, 0], [4, 61, 0], [0, 0, 15]]\n",
            "7   1999          0.989247     0.979987  [[23, 0, 0], [1, 57, 0], [0, 0, 12]]\n",
            "8   2000          0.989583     0.981210   [[32, 1, 0], [0, 54, 0], [0, 0, 9]]\n",
            "9   2001          0.931034     0.871807   [[22, 6, 0], [0, 50, 0], [0, 0, 9]]\n",
            "10  2002          0.915663     0.837800   [[24, 0, 0], [7, 47, 0], [0, 0, 5]]\n",
            "11  2003          0.893617     0.782307   [[20, 5, 0], [5, 57, 0], [0, 0, 7]]\n",
            "12  2004          0.976471     0.955844   [[28, 2, 0], [0, 49, 0], [0, 0, 6]]\n",
            "13  2005          0.893204     0.815742  [[30, 2, 0], [9, 50, 0], [0, 0, 12]]\n",
            "14  2006          0.925532     0.865714  [[20, 5, 0], [2, 53, 0], [0, 0, 14]]\n",
            "15  2007          0.941176     0.883028   [[27, 2, 0], [4, 62, 0], [0, 0, 7]]\n",
            "16  2008          0.869048     0.757417  [[25, 0, 0], [11, 43, 0], [0, 0, 5]]\n",
            "17  2009          0.975610     0.954241   [[23, 1, 0], [1, 49, 0], [0, 0, 8]]\n",
            "18  2010          0.940476     0.868462   [[21, 4, 0], [1, 55, 0], [0, 0, 3]]\n",
            "19  2011          0.925926     0.840296   [[23, 4, 0], [4, 70, 0], [0, 0, 7]]\n",
            "20  2012          0.970588     0.943553  [[25, 2, 0], [1, 63, 0], [0, 0, 11]]\n",
            "21  2013          0.917526     0.846580  [[21, 1, 0], [7, 56, 0], [0, 0, 12]]\n",
            "22  2014          0.966292     0.935304   [[24, 1, 0], [2, 54, 0], [0, 0, 8]]\n",
            "23  2015          0.968085     0.945433  [[36, 1, 0], [2, 45, 0], [0, 0, 10]]\n",
            "24  2016          0.923077     0.862389  [[22, 5, 0], [2, 50, 0], [0, 0, 12]]\n",
            "25  2017          0.901786     0.820617  [[28, 3, 0], [8, 61, 0], [0, 0, 12]]\n",
            "26  2018          0.942529     0.891952   [[22, 2, 0], [3, 51, 0], [0, 0, 9]]\n",
            "27  2019          0.938144     0.875000   [[21, 3, 0], [3, 61, 0], [0, 0, 9]]\n",
            "28  2020          0.939024     0.891821   [[26, 0, 0], [5, 43, 0], [0, 0, 8]]\n",
            "29  2021          0.876033     0.757093  [[28, 4, 0], [11, 69, 0], [0, 0, 9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_svm_df.to_csv('class_svm_results.csv')"
      ],
      "metadata": {
        "id": "F5of8ikfDWtW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SVM con RBF"
      ],
      "metadata": {
        "id": "io01_6BrSFDt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm_rbf_classifier = ee.Classifier.libsvm(kernelType='RBF')\n",
        "classified_images_svm_rbf, error_matrices_svm_rbf, overall_accuracies_svm_rbf, kappa_indexes_svm_rbf = classifyMultipleImages(svm_rbf_classifier, clipped_images, 0.80, training, label, bands)"
      ],
      "metadata": {
        "id": "nK7mnaalSJlQ",
        "outputId": "a7a7ac7f-f261-4624-b65f-385241ba03ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifying image 1 ...\n",
            "Classifying image 2 ...\n",
            "Classifying image 3 ...\n",
            "Classifying image 4 ...\n",
            "Classifying image 5 ...\n",
            "Classifying image 6 ...\n",
            "Classifying image 7 ...\n",
            "Classifying image 8 ...\n",
            "Classifying image 9 ...\n",
            "Classifying image 10 ...\n",
            "Classifying image 11 ...\n",
            "Classifying image 12 ...\n",
            "Classifying image 13 ...\n",
            "Classifying image 14 ...\n",
            "Classifying image 15 ...\n",
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            "Classifying image 28 ...\n",
            "Classifying image 29 ...\n",
            "Classifying image 30 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_dict = {\n",
        "    'year': years_of_interes,\n",
        "    'overall_accuracy': overall_accuracies_svm_rbf,\n",
        "    'kappa_index': kappa_indexes_svm_rbf,\n",
        "    'error_matrices': error_matrices_svm_rbf\n",
        "}\n",
        "\n",
        "result_svm_rbf_df = pd.DataFrame(result_dict)\n",
        "result_svm_rbf_df.head()"
      ],
      "metadata": {
        "id": "VIjh91aUSasi",
        "outputId": "da4dd62c-fe77-40dd-f8d9-7c9af227a8cb",
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          "height": 206
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  overall_accuracy  kappa_index                       error_matrices\n",
              "0  1991          0.606383     0.107748  [[0, 32, 0], [0, 54, 0], [0, 5, 3]]\n",
              "1  1993          0.604938     0.120163  [[0, 26, 0], [0, 46, 0], [0, 6, 3]]\n",
              "2  1994          0.645833     0.043096  [[0, 25, 0], [0, 61, 0], [0, 9, 1]]\n",
              "3  1995          0.740260     0.139665  [[0, 15, 0], [0, 55, 0], [0, 5, 2]]\n",
              "4  1996          0.723404     0.232412  [[0, 22, 0], [0, 63, 0], [0, 4, 5]]"
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              "      <th>0</th>\n",
              "      <td>1991</td>\n",
              "      <td>0.606383</td>\n",
              "      <td>0.107748</td>\n",
              "      <td>[[0, 32, 0], [0, 54, 0], [0, 5, 3]]</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1993</td>\n",
              "      <td>0.604938</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1994</td>\n",
              "      <td>0.645833</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995</td>\n",
              "      <td>0.740260</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1996</td>\n",
              "      <td>0.723404</td>\n",
              "      <td>0.232412</td>\n",
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        "id": "WvJTFQKRSgwJ",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              year  overall_accuracy  kappa_index\n",
              "count    30.000000         30.000000    30.000000\n",
              "mean   2006.466667          0.640962     0.091813\n",
              "std       8.861903          0.052583     0.089095\n",
              "min    1991.000000          0.510638     0.000000\n",
              "25%    1999.250000          0.607565     0.007351\n",
              "50%    2006.500000          0.648272     0.072781\n",
              "75%    2013.750000          0.665730     0.134804\n",
              "max    2021.000000          0.740260     0.290019"
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              "      <th>mean</th>\n",
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              "      <td>0.640962</td>\n",
              "      <td>0.091813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.861903</td>\n",
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              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
              "      <td>2006.500000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>2013.750000</td>\n",
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              "      <td>0.134804</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
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              "      <td>0.740260</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "  "
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    },
    {
      "cell_type": "code",
      "source": [
        "print(result_svm_rbf_df)"
      ],
      "metadata": {
        "id": "Ys51iYwDSira",
        "outputId": "6d7e0899-49d9-49d9-e0dc-64085686ad82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    year  overall_accuracy  kappa_index                        error_matrices\n",
            "0   1991          0.606383     0.107748   [[0, 32, 0], [0, 54, 0], [0, 5, 3]]\n",
            "1   1993          0.604938     0.120163   [[0, 26, 0], [0, 46, 0], [0, 6, 3]]\n",
            "2   1994          0.645833     0.043096   [[0, 25, 0], [0, 61, 0], [0, 9, 1]]\n",
            "3   1995          0.740260     0.139665   [[0, 15, 0], [0, 55, 0], [0, 5, 2]]\n",
            "4   1996          0.723404     0.232412   [[0, 22, 0], [0, 63, 0], [0, 4, 5]]\n",
            "5   1997          0.560748     0.030087   [[0, 38, 0], [0, 59, 0], [0, 9, 1]]\n",
            "6   1998          0.611111     0.033660  [[0, 28, 0], [0, 65, 0], [0, 14, 1]]\n",
            "7   1999          0.655914     0.122900   [[0, 23, 0], [0, 58, 0], [0, 9, 3]]\n",
            "8   2000          0.572917     0.034584   [[0, 33, 0], [0, 54, 0], [0, 8, 1]]\n",
            "9   2001          0.574713     0.000000   [[0, 28, 0], [0, 50, 0], [0, 9, 0]]\n",
            "10  2002          0.662651     0.053746   [[0, 24, 0], [0, 54, 0], [0, 4, 1]]\n",
            "11  2003          0.659574     0.000000   [[0, 25, 0], [0, 62, 0], [0, 7, 0]]\n",
            "12  2004          0.576471     0.000000   [[0, 30, 0], [0, 49, 0], [0, 6, 0]]\n",
            "13  2005          0.572816     0.000000  [[0, 32, 0], [0, 59, 0], [0, 12, 0]]\n",
            "14  2006          0.659574     0.239059   [[0, 25, 0], [0, 55, 0], [0, 7, 7]]\n",
            "15  2007          0.647059     0.000000   [[0, 29, 0], [0, 66, 0], [0, 7, 0]]\n",
            "16  2008          0.654762     0.051771   [[0, 25, 0], [0, 54, 0], [0, 4, 1]]\n",
            "17  2009          0.646341     0.135273   [[0, 24, 0], [0, 50, 0], [0, 5, 3]]\n",
            "18  2010          0.666667     0.000000   [[0, 25, 0], [0, 56, 0], [0, 3, 0]]\n",
            "19  2011          0.712963     0.135554   [[0, 27, 0], [0, 74, 0], [0, 4, 3]]\n",
            "20  2012          0.627451     0.000000  [[0, 27, 0], [0, 64, 0], [0, 11, 0]]\n",
            "21  2013          0.649485     0.000000  [[0, 22, 0], [0, 63, 0], [0, 12, 0]]\n",
            "22  2014          0.662921     0.133398   [[0, 25, 0], [0, 56, 0], [0, 5, 3]]\n",
            "23  2015          0.510638     0.029405   [[0, 37, 0], [0, 47, 0], [0, 9, 1]]\n",
            "24  2016          0.637363     0.207443   [[0, 27, 0], [0, 52, 0], [0, 6, 6]]\n",
            "25  2017          0.696429     0.285419   [[0, 31, 0], [0, 69, 0], [0, 3, 9]]\n",
            "26  2018          0.701149     0.290019   [[0, 24, 0], [0, 54, 0], [0, 2, 7]]\n",
            "27  2019          0.680412     0.091815   [[0, 24, 0], [0, 64, 0], [0, 7, 2]]\n",
            "28  2020          0.621951     0.125860   [[0, 26, 0], [0, 48, 0], [0, 5, 3]]\n",
            "29  2021          0.685950     0.111326   [[0, 32, 0], [0, 80, 0], [0, 6, 3]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_svm_rbf_df.to_csv('class_svm_rbf_results.csv')"
      ],
      "metadata": {
        "id": "BXEg7ODZSlTg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualización de mapas"
      ],
      "metadata": {
        "id": "XPTxUAk-d-WY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def mapSingleLayer(layer, layar_vis_params, layer_name, folium_width='100%', folium_height=None):\n",
        "\n",
        "  f = folium.Figure(width=folium_width, height=folium_height)\n",
        "\n",
        "  my_map = folium.Map(location=[amss_lat, amss_lon], zoom_start=10, max_bounds=True, min_zoom=1.5).add_to(f)\n",
        "\n",
        "  # Boundaries layer for our ROI\n",
        "  my_map.add_ee_layer(roi_fc.draw('006600', 3, 2), {}, 'ROI Boundaries')\n",
        "\n",
        "  # Layer of interest\n",
        "  my_map.add_ee_layer(layer, layar_vis_params, layer_name)\n",
        "  \n",
        "  my_map.add_child(folium.LayerControl())\n",
        "\n",
        "  display(my_map)"
      ],
      "metadata": {
        "id": "IMGG7qNnwwf_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def mapClippedAndClassifiedForAYear(year, years, clipped, clipped_params, classified, classified_params, folium_width='100%', folium_height=None):\n",
        "\n",
        "  f = folium.Figure(width=folium_width, height=folium_height)\n",
        "\n",
        "  my_map = folium.Map(location=[amss_lat, amss_lon], zoom_start=10, max_bounds=True, min_zoom=1.5).add_to(f)\n",
        "\n",
        "  # Boundaries layer\n",
        "  my_map.add_ee_layer(roi_fc.draw('006600', 3, 2), {}, 'ROI Boundaries')\n",
        "  \n",
        "  # Get index for desired year\n",
        "  index = years.index(year)\n",
        "\n",
        "  # Clipped Image layer\n",
        "  my_map.add_ee_layer(clipped[index], clipped_params, 'Landsat ' + str(year))\n",
        "  \n",
        "  # Classified image mayer\n",
        "  my_map.add_ee_layer(classified[index], classified_params, 'Classification ' + str(year))\n",
        "\n",
        "  # Add a layer control panel to the map\n",
        "  my_map.add_child(folium.LayerControl())\n",
        "\n",
        "  display(my_map)"
      ],
      "metadata": {
        "id": "PUZjgnN9wxxn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set visualization parameters for Landsat 8 images\n",
        "vis_params_L8 = {\n",
        "    'bands':['B4', 'B3', 'B2'], \n",
        "    'min': 0, \n",
        "    'max': 3000, \n",
        "    'gamma': 1.4\n",
        "}\n",
        "\n",
        "# Set visualization parameters for Landsat 4/5/7 images\n",
        "vis_params_L457 = {\n",
        "    'bands':['B3', 'B2', 'B1'], \n",
        "    'min': 0, \n",
        "    'max': 3000, \n",
        "    'gamma': 1.4\n",
        "}\n",
        "\n",
        "# Define palette for the clasification\n",
        "land_cover_palette = [\n",
        "                      '#E31A1C', # urban (0)\n",
        "                      '#005A32', # land  (1)\n",
        "                      '#0C2C84', # water (2)\n",
        "]\n",
        "\n",
        "# Set visualization parameters for classification layers\n",
        "land_cover_params = {\n",
        "    'palette': land_cover_palette, \n",
        "    'min': 0, \n",
        "    'max': 2\n",
        "}"
      ],
      "metadata": {
        "id": "s4qVEEDO4FdP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mapClippedAndClassifiedForAYear(1991, years_of_interes, clipped_images, vis_params_L457, classified_images_rf, land_cover_params, '75%', 500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "OidAzVXPLzKM",
        "outputId": "ffa53ecf-5a64-4954-84de-6ec3b9d8c705"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f43e3644a90>"
            ],
            "text/html": [
              "<iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_8436cab8212572251a726dc57451bca0 {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_8436cab8212572251a726dc57451bca0&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var southWest = L.latLng(-90, -180);\n",
              "        var northEast = L.latLng(90, 180);\n",
              "        var bounds = L.latLngBounds(southWest, northEast);\n",
              "    \n",
              "\n",
              "    var map_8436cab8212572251a726dc57451bca0 = L.map(\n",
              "        &#x27;map_8436cab8212572251a726dc57451bca0&#x27;, {\n",
              "        center: [13.72356, -89.214763],\n",
              "        zoom: 10,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_03b4dfd7d2e8a2014437ec561d278eb5 = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 1.5,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_8436cab8212572251a726dc57451bca0);\n",
              "    var tile_layer_c26f2a6cd79b849ee319abee0bdbc38f = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/f6a616aab17f0ed5506b468e5e4945ff-604dd8d6af72d10cd9c3b81dc24fd514/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_8436cab8212572251a726dc57451bca0);\n",
              "    var tile_layer_4d4aa2b3061f14228e122ccd70678e78 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/01182dfbcbe2ea50fc04c3d9bf253d6e-562d5491276a49f29dbd33966812ac12/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_8436cab8212572251a726dc57451bca0);\n",
              "    var tile_layer_c35aa0443ad7377878c0a60345003f4f = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/faf7cd6062cc698619745ca40ae66878-7bb8047f1c96320b3186cd5f04f917ac/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_8436cab8212572251a726dc57451bca0);\n",
              "    \n",
              "            var layer_control_131f013316613dbb0243097cfc5113e9 = {\n",
              "                base_layers : { &quot;openstreetmap&quot; : tile_layer_03b4dfd7d2e8a2014437ec561d278eb5, },\n",
              "                overlays : { &quot;ROI Boundaries&quot; : tile_layer_c26f2a6cd79b849ee319abee0bdbc38f,&quot;Landsat 1991&quot; : tile_layer_4d4aa2b3061f14228e122ccd70678e78,&quot;Classification 1991&quot; : tile_layer_c35aa0443ad7377878c0a60345003f4f, }\n",
              "                };\n",
              "            L.control.layers(\n",
              "                layer_control_131f013316613dbb0243097cfc5113e9.base_layers,\n",
              "                layer_control_131f013316613dbb0243097cfc5113e9.overlays,\n",
              "                {position: &#x27;topright&#x27;,\n",
              "                 collapsed: true,\n",
              "                 autoZIndex: true\n",
              "                }).addTo(map_8436cab8212572251a726dc57451bca0);\n",
              "            \n",
              "        \n",
              "&lt;/script&gt;\" width=\"75%\" height=\"500\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Capas adicionales para modelo de predicción\n"
      ],
      "metadata": {
        "id": "YXw2Ax7u6AGy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Digital Elevation Model (DEM)\n",
        "dem = ee.Image(\"USGS/SRTMGL1_003\").clip(roi_geom)\n",
        "dem_vis_params = {'min': 0, 'max': 6000}\n",
        "mapSingleLayer(dem, dem_vis_params, 'DEM AMSSS', '75%', 500)"
      ],
      "metadata": {
        "id": "mDHqa7yW5_LT",
        "outputId": "b8bb326d-8cdb-4409-fd13-43d0873975f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f43e2b79b90>"
            ],
            "text/html": [
              "<iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_f5b50c46cf9a89ab238e7d8e495a679b {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_f5b50c46cf9a89ab238e7d8e495a679b&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var southWest = L.latLng(-90, -180);\n",
              "        var northEast = L.latLng(90, 180);\n",
              "        var bounds = L.latLngBounds(southWest, northEast);\n",
              "    \n",
              "\n",
              "    var map_f5b50c46cf9a89ab238e7d8e495a679b = L.map(\n",
              "        &#x27;map_f5b50c46cf9a89ab238e7d8e495a679b&#x27;, {\n",
              "        center: [13.72356, -89.214763],\n",
              "        zoom: 10,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_d137fc53b9251a85761d275845a014b5 = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 1.5,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_f5b50c46cf9a89ab238e7d8e495a679b);\n",
              "    var tile_layer_b832b4a8ebaeb23ab78d1078ab8b0d71 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/f6a616aab17f0ed5506b468e5e4945ff-a9e4622f5a6bfa25d44a1725fbc815cd/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_f5b50c46cf9a89ab238e7d8e495a679b);\n",
              "    var tile_layer_f64d74f2780e3d14d93f3804d51108bb = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/3d7c3f53ab877b6f7fad9eceee40415f-99d858411f773f3dbb0e88a634fc3c54/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_f5b50c46cf9a89ab238e7d8e495a679b);\n",
              "    \n",
              "            var layer_control_34aac3f0b0af0ba1b3356c4647014413 = {\n",
              "                base_layers : { &quot;openstreetmap&quot; : tile_layer_d137fc53b9251a85761d275845a014b5, },\n",
              "                overlays : { &quot;ROI Boundaries&quot; : tile_layer_b832b4a8ebaeb23ab78d1078ab8b0d71,&quot;DEM AMSSS&quot; : tile_layer_f64d74f2780e3d14d93f3804d51108bb, }\n",
              "                };\n",
              "            L.control.layers(\n",
              "                layer_control_34aac3f0b0af0ba1b3356c4647014413.base_layers,\n",
              "                layer_control_34aac3f0b0af0ba1b3356c4647014413.overlays,\n",
              "                {position: &#x27;topright&#x27;,\n",
              "                 collapsed: true,\n",
              "                 autoZIndex: true\n",
              "                }).addTo(map_f5b50c46cf9a89ab238e7d8e495a679b);\n",
              "            \n",
              "        \n",
              "&lt;/script&gt;\" width=\"75%\" height=\"500\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Slope\n",
        "elevation = dem.select('elevation')\n",
        "slope = ee.Terrain.slope(elevation);\n",
        "slope_vis_params = {'min': 0, 'max': 60}\n",
        "mapSingleLayer(slope, slope_vis_params, 'Slope AMSSS', '75%', 500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "64DSM0Zx9CK8",
        "outputId": "f0fc976f-84f2-417e-dec8-1bb9f4f1ed63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f43e3846b50>"
            ],
            "text/html": [
              "<iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_4c91c311ec79c2c3a007c53863a14373 {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_4c91c311ec79c2c3a007c53863a14373&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var southWest = L.latLng(-90, -180);\n",
              "        var northEast = L.latLng(90, 180);\n",
              "        var bounds = L.latLngBounds(southWest, northEast);\n",
              "    \n",
              "\n",
              "    var map_4c91c311ec79c2c3a007c53863a14373 = L.map(\n",
              "        &#x27;map_4c91c311ec79c2c3a007c53863a14373&#x27;, {\n",
              "        center: [13.72356, -89.214763],\n",
              "        zoom: 10,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_8514873a7bfbeedc5eba4b5622a65e0b = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 1.5,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_4c91c311ec79c2c3a007c53863a14373);\n",
              "    var tile_layer_961259998415a7df0adbf6b54f1dc698 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/f6a616aab17f0ed5506b468e5e4945ff-09f5f43cc347002f0e521768cb1794f0/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_4c91c311ec79c2c3a007c53863a14373);\n",
              "    var tile_layer_5801b59e7bef3115b33f5f631a644d04 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/48e09bead3bbb54865f3a913edd05556-dbe68533bd9b43f92516b2ec78ad213e/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_4c91c311ec79c2c3a007c53863a14373);\n",
              "    \n",
              "            var layer_control_ff162fd45771b3bc1e30086329b85b66 = {\n",
              "                base_layers : { &quot;openstreetmap&quot; : tile_layer_8514873a7bfbeedc5eba4b5622a65e0b, },\n",
              "                overlays : { &quot;ROI Boundaries&quot; : tile_layer_961259998415a7df0adbf6b54f1dc698,&quot;Slope AMSSS&quot; : tile_layer_5801b59e7bef3115b33f5f631a644d04, }\n",
              "                };\n",
              "            L.control.layers(\n",
              "                layer_control_ff162fd45771b3bc1e30086329b85b66.base_layers,\n",
              "                layer_control_ff162fd45771b3bc1e30086329b85b66.overlays,\n",
              "                {position: &#x27;topright&#x27;,\n",
              "                 collapsed: true,\n",
              "                 autoZIndex: true\n",
              "                }).addTo(map_4c91c311ec79c2c3a007c53863a14373);\n",
              "            \n",
              "        \n",
              "&lt;/script&gt;\" width=\"75%\" height=\"500\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Aspect\n",
        "aspect = ee.Terrain.aspect(elevation);\n",
        "aspect_vis_params = {'min': 0, 'max': 359.99}\n",
        "mapSingleLayer(aspect, aspect_vis_params, 'Aspect AMSSS', '75%', 500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "oHTL5bdI9GEp",
        "outputId": "ef6cd5db-e6bc-4212-dc3b-fb77cad0a85a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f43e358a510>"
            ],
            "text/html": [
              "<iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_b4099ea1635014d0321a7ac9ccce793c {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_b4099ea1635014d0321a7ac9ccce793c&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var southWest = L.latLng(-90, -180);\n",
              "        var northEast = L.latLng(90, 180);\n",
              "        var bounds = L.latLngBounds(southWest, northEast);\n",
              "    \n",
              "\n",
              "    var map_b4099ea1635014d0321a7ac9ccce793c = L.map(\n",
              "        &#x27;map_b4099ea1635014d0321a7ac9ccce793c&#x27;, {\n",
              "        center: [13.72356, -89.214763],\n",
              "        zoom: 10,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_01d6796c0385dc378a5dc7893ef8685d = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 1.5,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_b4099ea1635014d0321a7ac9ccce793c);\n",
              "    var tile_layer_ea3d859084d379be16835ec20394bb27 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/f6a616aab17f0ed5506b468e5e4945ff-492d9e08f568d2874f0ef9f18dd8e337/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_b4099ea1635014d0321a7ac9ccce793c);\n",
              "    var tile_layer_b7c277d14c9efbec5752cfc665ed8be5 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/3cc25c31801ed25f497a8c46b3839e8b-052db40a65235d4dd5345307ea0450d8/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_b4099ea1635014d0321a7ac9ccce793c);\n",
              "    \n",
              "            var layer_control_e6377fc62e6deb87b0517f59117770c7 = {\n",
              "                base_layers : { &quot;openstreetmap&quot; : tile_layer_01d6796c0385dc378a5dc7893ef8685d, },\n",
              "                overlays : { &quot;ROI Boundaries&quot; : tile_layer_ea3d859084d379be16835ec20394bb27,&quot;Aspect AMSSS&quot; : tile_layer_b7c277d14c9efbec5752cfc665ed8be5, }\n",
              "                };\n",
              "            L.control.layers(\n",
              "                layer_control_e6377fc62e6deb87b0517f59117770c7.base_layers,\n",
              "                layer_control_e6377fc62e6deb87b0517f59117770c7.overlays,\n",
              "                {position: &#x27;topright&#x27;,\n",
              "                 collapsed: true,\n",
              "                 autoZIndex: true\n",
              "                }).addTo(map_b4099ea1635014d0321a7ac9ccce793c);\n",
              "            \n",
              "        \n",
              "&lt;/script&gt;\" width=\"75%\" height=\"500\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Hillshade\n",
        "terrain = ee.Terrain.products(dem)\n",
        "hillshade = terrain.select('hillshade')\n",
        "hillshade_vis_params = {min: 0, max: 255}\n",
        "mapSingleLayer(hillshade, hillshade_vis_params, 'Hillshade AMSSS', '75%', 500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "2nH5I9uc9xWM",
        "outputId": "b8438449-e0ff-4ed2-a06b-dc11da05ff51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f43e479cfd0>"
            ],
            "text/html": [
              "<iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    &lt;script&gt;L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css&quot;/&gt;\n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    \n",
              "    &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "        initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "    &lt;style&gt;#map_887978b75413bbda65977fbec5c7e834 {\n",
              "        position: relative;\n",
              "        width: 100.0%;\n",
              "        height: 100.0%;\n",
              "        left: 0.0%;\n",
              "        top: 0.0%;\n",
              "        }\n",
              "    &lt;/style&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "    &lt;div class=&quot;folium-map&quot; id=&quot;map_887978b75413bbda65977fbec5c7e834&quot; &gt;&lt;/div&gt;\n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "    \n",
              "        var southWest = L.latLng(-90, -180);\n",
              "        var northEast = L.latLng(90, 180);\n",
              "        var bounds = L.latLngBounds(southWest, northEast);\n",
              "    \n",
              "\n",
              "    var map_887978b75413bbda65977fbec5c7e834 = L.map(\n",
              "        &#x27;map_887978b75413bbda65977fbec5c7e834&#x27;, {\n",
              "        center: [13.72356, -89.214763],\n",
              "        zoom: 10,\n",
              "        maxBounds: bounds,\n",
              "        layers: [],\n",
              "        worldCopyJump: false,\n",
              "        crs: L.CRS.EPSG3857,\n",
              "        zoomControl: true,\n",
              "        });\n",
              "\n",
              "\n",
              "    \n",
              "    var tile_layer_86d1e2b80f48d82f885b92a49b8942c2 = L.tileLayer(\n",
              "        &#x27;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: null,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 1.5,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_887978b75413bbda65977fbec5c7e834);\n",
              "    var tile_layer_9d986b0c2d9b2537bc36795681383bd3 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/f6a616aab17f0ed5506b468e5e4945ff-a3cbc97cedcf0bfdbc37005d91255d92/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_887978b75413bbda65977fbec5c7e834);\n",
              "    var tile_layer_3cb34fdf7f49829265ab08112007cfd8 = L.tileLayer(\n",
              "        &#x27;https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/fb1c8888a13a1663c6d1baf6c8300504-15bfb52491c21311b31edae8bf41bb66/tiles/{z}/{x}/{y}&#x27;,\n",
              "        {\n",
              "        &quot;attribution&quot;: &quot;Map Data &amp;copy; &lt;a href=\\&quot;https://earthengine.google.com/\\&quot;&gt;Google Earth Engine&lt;/a&gt;&quot;,\n",
              "        &quot;detectRetina&quot;: false,\n",
              "        &quot;maxNativeZoom&quot;: 18,\n",
              "        &quot;maxZoom&quot;: 18,\n",
              "        &quot;minZoom&quot;: 0,\n",
              "        &quot;noWrap&quot;: false,\n",
              "        &quot;opacity&quot;: 1,\n",
              "        &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "        &quot;tms&quot;: false\n",
              "}).addTo(map_887978b75413bbda65977fbec5c7e834);\n",
              "    \n",
              "            var layer_control_2b4d5e7462eb4f922e8bde7b7b66af10 = {\n",
              "                base_layers : { &quot;openstreetmap&quot; : tile_layer_86d1e2b80f48d82f885b92a49b8942c2, },\n",
              "                overlays : { &quot;ROI Boundaries&quot; : tile_layer_9d986b0c2d9b2537bc36795681383bd3,&quot;Hillshade AMSSS&quot; : tile_layer_3cb34fdf7f49829265ab08112007cfd8, }\n",
              "                };\n",
              "            L.control.layers(\n",
              "                layer_control_2b4d5e7462eb4f922e8bde7b7b66af10.base_layers,\n",
              "                layer_control_2b4d5e7462eb4f922e8bde7b7b66af10.overlays,\n",
              "                {position: &#x27;topright&#x27;,\n",
              "                 collapsed: true,\n",
              "                 autoZIndex: true\n",
              "                }).addTo(map_887978b75413bbda65977fbec5c7e834);\n",
              "            \n",
              "        \n",
              "&lt;/script&gt;\" width=\"75%\" height=\"500\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exportando imagenes como GeoTIFF"
      ],
      "metadata": {
        "id": "rIw0hiXtoLI-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Path where geotiff files are saved\n",
        "drive_path = \"GeoTIFF-Molusce-Final\""
      ],
      "metadata": {
        "id": "hDOODObeoU1J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Export the image, specifying scale and region.\n",
        "def exportImageToDrive(image_to_export, name, drive_folder, image_scale, region, crs):\n",
        "\n",
        "  task = ee.batch.Export.image.toDrive(**{\n",
        "      'image': image_to_export,\n",
        "      'description': name,\n",
        "      'folder': drive_folder,\n",
        "      'crs': crs,\n",
        "      'scale': image_scale,\n",
        "      'region': region\n",
        "  })\n",
        "  task.start()\n",
        "\n",
        "  while task.active():\n",
        "    print('Polling for task (id: {}).'.format(task.id))\n",
        "    time.sleep(10)"
      ],
      "metadata": {
        "id": "31txlnx_ocK0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def exportImagesInRangeToDrive(images, range_list):\n",
        "  for i in range(len(range_list)):\n",
        "    name = 'LC01T1SR_RF_' + str(range_list[i])\n",
        "    print(\"Exporting\", name)\n",
        "    exportImageToDrive(images[i], name, drive_path, 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "Y1wUH-ZIq5dK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exportando imágenes clasificadas."
      ],
      "metadata": {
        "id": "WmtXgHU_7GDB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "exportImageToDrive(classified_images_rf[0], 'LULC_RF_1991', drive_path, 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "lD1x0xg_A59X",
        "outputId": "1612dcc3-96b6-4118-9ada-3f97f6d93c63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n",
            "Polling for task (id: OMFGDYMH2ZYN5WBGUJSY464D).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exportImageToDrive(classified_images_rf[13], 'LULC_RF_2005', drive_path, 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "b2jTB8HqXFN1",
        "outputId": "1dfae390-8478-4c6b-95a4-8aea7de83c59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n",
            "Polling for task (id: AXFOHEQGQQTW5ZIKGAUEYNWN).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exportImageToDrive(classified_images_rf[27], 'LULC_RF_2019', drive_path, 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "FP0FKZ7TXN4X",
        "outputId": "46d4c507-f6ca-4941-f57e-81b0c8e255d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n",
            "Polling for task (id: DPJG2BBZDPG76PCQNFSU62VH).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exportImagesInRangeToDrive(classified_images_rf, years_of_interes)"
      ],
      "metadata": {
        "id": "oY002lnJXciw",
        "outputId": "e2c27f2f-945d-4d3c-baee-db2bf8fc713a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Exporting LC01T1SR_RF_1991\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Polling for task (id: MWSMFJ57P2Y7X2ZEPF36LNEK).\n",
            "Exporting LC01T1SR_RF_1993\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Polling for task (id: FUAFHW5NPCC7EAD4PUPSOTJU).\n",
            "Exporting LC01T1SR_RF_1994\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Polling for task (id: 7CQX2JBBYP26BF6DJAQVOKPF).\n",
            "Exporting LC01T1SR_RF_1995\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Polling for task (id: WY6T22PWEQC5ICMGEPCXP2F7).\n",
            "Exporting LC01T1SR_RF_1996\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Polling for task (id: KROMSKJX44MYPET2KC7YNBG2).\n",
            "Exporting LC01T1SR_RF_1997\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Polling for task (id: RKJOIPEP3UY5ITDQ6XLREMQL).\n",
            "Exporting LC01T1SR_RF_1998\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Polling for task (id: 7Q7M773VE6VRWCVGYH6B4RCE).\n",
            "Exporting LC01T1SR_RF_1999\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Polling for task (id: BUWBYLQKET7A37IODSOOFJVY).\n",
            "Exporting LC01T1SR_RF_2000\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Polling for task (id: RXHJIBCPAFHYBAER4GEVAV24).\n",
            "Exporting LC01T1SR_RF_2001\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Polling for task (id: DXGWG7F34RJCBTFPVKEOH62S).\n",
            "Exporting LC01T1SR_RF_2002\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Polling for task (id: 5KYGGASMLUOSV7OAUQ7LLEFU).\n",
            "Exporting LC01T1SR_RF_2003\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Polling for task (id: SY2RN4IGJFHX3RAVO3F2N4XV).\n",
            "Exporting LC01T1SR_RF_2004\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Polling for task (id: VRMSIW3KOXDUF2JPA56S2Z6Z).\n",
            "Exporting LC01T1SR_RF_2005\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Polling for task (id: 4SLFNA7NWBUXB37VYOF7U2QY).\n",
            "Exporting LC01T1SR_RF_2006\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Polling for task (id: R6CQ5QLUVCQXU54GUFQHW6CT).\n",
            "Exporting LC01T1SR_RF_2007\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Polling for task (id: YZZS6XPQKF3VI2DP4JVW47BZ).\n",
            "Exporting LC01T1SR_RF_2008\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Polling for task (id: E4QCQLAEOAXQC7IS5WFXP7U7).\n",
            "Exporting LC01T1SR_RF_2009\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Polling for task (id: WMCDJ3TVTCXMJZS3LLIVAJ76).\n",
            "Exporting LC01T1SR_RF_2010\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Polling for task (id: YQHC2BNC222DCZZR4HVDSXUD).\n",
            "Exporting LC01T1SR_RF_2011\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Polling for task (id: NC56SZPDXZX7HCSFPAV6RMTW).\n",
            "Exporting LC01T1SR_RF_2012\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Polling for task (id: DYG63K5MNZLWTP5MFNEHY7PA).\n",
            "Exporting LC01T1SR_RF_2013\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Polling for task (id: 4MPDYHYX4Y4E5RVCC6XX6EZD).\n",
            "Exporting LC01T1SR_RF_2014\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Polling for task (id: MUNPY6DW3XJ7FC4VTBPCPPVF).\n",
            "Exporting LC01T1SR_RF_2015\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Polling for task (id: 5VXRFDPAMSTBYRTSSTEBJTXB).\n",
            "Exporting LC01T1SR_RF_2016\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Polling for task (id: XWGUW55YNOCMXR23UYOQLJP4).\n",
            "Exporting LC01T1SR_RF_2017\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Polling for task (id: R7WKS4KVKYM6OJX7TVXWFM7Q).\n",
            "Exporting LC01T1SR_RF_2018\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Polling for task (id: RI3LB37R5LKPGLGHFL7KP2I2).\n",
            "Exporting LC01T1SR_RF_2019\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Polling for task (id: NBZ33TLT4BF25GTV5NICXK2H).\n",
            "Exporting LC01T1SR_RF_2020\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Polling for task (id: CC3AGD77ZYUF3ZGICS7F7L5E).\n",
            "Exporting LC01T1SR_RF_2021\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n",
            "Polling for task (id: UHDI3TWE5Y4TAHFU7ITIE47L).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exportando imagenes DEM."
      ],
      "metadata": {
        "id": "DMCiamac7KcS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "exportImageToDrive(dem, 'DEM_AMSS', 'DEM-FINAL', 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "AnsUViev7NjZ",
        "outputId": "88257ad2-1618-4fbb-ec53-366a760598f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: NJ55Z74FY67GOGBXK6LN7CIM).\n",
            "Polling for task (id: NJ55Z74FY67GOGBXK6LN7CIM).\n",
            "Polling for task (id: NJ55Z74FY67GOGBXK6LN7CIM).\n",
            "Polling for task (id: NJ55Z74FY67GOGBXK6LN7CIM).\n",
            "Polling for task (id: NJ55Z74FY67GOGBXK6LN7CIM).\n",
            "Polling for task (id: NJ55Z74FY67GOGBXK6LN7CIM).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exportImageToDrive(slope, 'SLOPE_AMSS', 'DEM-FINAL', 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "xm7KzgwO7dDj",
        "outputId": "1ae26693-cf35-4418-8f17-0b4d17dfe179",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: TPATVF3I6RPXWZRJL3E6M7JM).\n",
            "Polling for task (id: TPATVF3I6RPXWZRJL3E6M7JM).\n",
            "Polling for task (id: TPATVF3I6RPXWZRJL3E6M7JM).\n",
            "Polling for task (id: TPATVF3I6RPXWZRJL3E6M7JM).\n",
            "Polling for task (id: TPATVF3I6RPXWZRJL3E6M7JM).\n",
            "Polling for task (id: TPATVF3I6RPXWZRJL3E6M7JM).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exportImageToDrive(aspect, 'ASPECT_AMSS', 'DEM-FINAL', 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "6WZGAXeJ7eGJ",
        "outputId": "c9967660-ba59-4fd1-b2a9-6a6f14a6e4ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: FK6JPGKRPP3RPF5SMQ4XYV3N).\n",
            "Polling for task (id: FK6JPGKRPP3RPF5SMQ4XYV3N).\n",
            "Polling for task (id: FK6JPGKRPP3RPF5SMQ4XYV3N).\n",
            "Polling for task (id: FK6JPGKRPP3RPF5SMQ4XYV3N).\n",
            "Polling for task (id: FK6JPGKRPP3RPF5SMQ4XYV3N).\n",
            "Polling for task (id: FK6JPGKRPP3RPF5SMQ4XYV3N).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exportImageToDrive(hillshade, 'HILLSHADE_AMSS', 'DEM-FINAL', 30, roi_geom, 'EPSG:4326')"
      ],
      "metadata": {
        "id": "8jlQPAWe7ecR",
        "outputId": "0077dd82-660f-4d35-8338-a1f0daf5c31c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polling for task (id: IYKLINCP2ROKOSKZBHIALCIN).\n",
            "Polling for task (id: IYKLINCP2ROKOSKZBHIALCIN).\n",
            "Polling for task (id: IYKLINCP2ROKOSKZBHIALCIN).\n",
            "Polling for task (id: IYKLINCP2ROKOSKZBHIALCIN).\n",
            "Polling for task (id: IYKLINCP2ROKOSKZBHIALCIN).\n"
          ]
        }
      ]
    }
  ]
}